David Sathuluri is a Research Associate and Dr. Marco Tedesco is a Lamont Research Professor at the Lamont-Doherty Earth Observatory of Columbia University.
As climate scientists warn that we are approaching irreversible tipping points in the Earth’s climate system, paradoxically the very technologies being deployed to detect these tipping points – often based on AI – are exacerbating the problem, via acceleration of the associated energy consumption.
The UK’s much-celebrated £81-million ($109-million) Forecasting Tipping Points programme involving 27 teams, led by the Advanced Research + Invention Agency (ARIA), represents a contemporary faith in technological salvation – yet it embodies a profound contradiction. The ARIA programme explicitly aims to “harness the laws of physics and artificial intelligence to pick up subtle early warning signs of tipping” through advanced modelling.
We are deploying massive computational infrastructure to warn us of climate collapse while these same systems consume the energy and water resources needed to prevent or mitigate it. We are simultaneously investing in computationally intensive AI systems to monitor whether we will cross irreversible climate tipping points, even as these same AI systems could fuel that transition.
The computational cost of monitoring
Training a single large language model like GPT-3 consumed approximately 1,287 megawatt-hours of electricity, resulting in 552 metric tons of carbon dioxide – equivalent to driving 123 gasoline-powered cars for a year, according to a recent study.
GPT-4 required roughly 50 times more electricity. As the computational power needed for AI continues to double approximately every 100 days, the energy footprint of these systems is not static but is exponentially accelerating.
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And the environmental consequences of AI models extend far beyond electricity usage. Besides massive amounts of electricity (much of which is still fossil-fuel-based), such systems require advanced cooling that consumes enormous quantities of water, and sophisticated infrastructure that must be manufactured, transported, and deployed globally.
The water-energy nexus in climate-vulnerable regions
A single data center can consume up to 5 million gallons of drinking water per day – sufficient to supply thousands of households or farms. In the Phoenix area of the US alone, more than 58 data centers consume an estimated 170 million gallons of drinking water daily for cooling.
The geographical distribution of this infrastructure matters profoundly as data centers requiring high rates of mechanical cooling are disproportionately located in water-stressed and socioeconomically vulnerable regions, particularly in Asia-Pacific and Africa.
At the same time, we are deploying AI-intensive early warning systems to monitor climate tipping points in regions like Greenland, the Arctic, and the Atlantic circulation system – regions already experiencing catastrophic climate impacts. They represent thresholds that, once crossed, could trigger irreversible changes within decades, scientists have warned.
Yet computational models and AI-driven early warning systems operate according to different temporal logics. They promise to provide warnings that enable future action, but they consume energy – and therefore contribute to emissions – in the present.
This is not merely a technical problem to be solved with renewable energy deployment; it reflects a fundamental misalignment between the urgency of climate tipping points and the gradualist assumptions embedded in technological solutions.
The carbon budget concept reveals that there is a cumulative effect on how emissions impact on temperature rise, with significant lags between atmospheric concentration and temperature impact. Every megawatt-hour consumed by AI systems training on climate models today directly reduces the available carbon budget for tomorrow – including the carbon budget available for the energy transition itself.
The governance void
The deeper issue is that governance frameworks for AI development have completely decoupled from carbon budgets and tipping point timescales. UK AI regulation focuses on how much computing power AI systems use, but it does not require developers to ask: is this AI’s carbon footprint small enough to fit within our carbon budget for preventing climate tipping points?
There is no mechanism requiring that AI infrastructure deployment decisions account for the specific carbon budgets associated with preventing different categories of tipping points.
Meanwhile, the energy transition itself – renewable capacity expansion, grid modernization, electrification of transport – requires computation and data management. If we allow unconstrained AI expansion, we risk the perverse outcome in which computing infrastructure consumes the surplus renewable energy that could otherwise accelerate decarbonization, rather than enabling it.
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What would it mean to resolve the paradox?
Resolving this paradox requires, for example, moving beyond the assumption that technological solutions can be determined in isolation from carbon constraints. It demands several interventions:
First, any AI-driven climate monitoring system must operate within an explicitly defined carbon budget that directly reflects the tipping-point timescale it aims to detect. If we are attempting to provide warnings about tipping points that could be triggered within 10-20 years, the AI system’s carbon footprint must be evaluated against a corresponding carbon budget for that period.
Second, governance frameworks for AI development must explicitly incorporate climate-tipping point science, establishing threshold restrictions on computational intensity in relation to carbon budgets and renewable energy availability. This is not primarily a “sustainability” question; it is a justice and efficacy question.
Third, alternative models must be prioritized over the current trajectory toward ever-larger models. These should include approaches that integrate human expertise with AI in time-sensitive scenarios, carbon-aware model training, and using specialized processors matched to specific computational tasks rather than relying on universal energy-intensive systems.
The deeper critique
The fundamental issue is that the energy-system tipping point paradox reflects a broader crisis in how wealthy nations approach climate governance. We have faith that innovation and science can solve fundamental contradictions, rather than confronting the structural need to constrain certain forms of energy consumption and wealth accumulation. We would rather invest £81 million in computational systems to detect tipping points than make the political decisions required to prevent them.
The positive tipping point for energy transition exists – renewable energy is now cheaper than fossil fuels, and deployment rates are accelerating. What we lack is not technological capacity but political will to rapidly decarbonize, as well as community participation.
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Deploying energy-intensive AI systems to monitor tipping points while simultaneously failing to deploy available renewable energy represents a kind of technological distraction from the actual political choices required.
The paradox is thus also a warning: in the time remaining before irreversible tipping points are triggered, we must choose between building ever-more sophisticated systems to monitor climate collapse or deploying available resources – capital, energy, expertise, political attention – toward allaying the threat.
The post Using energy-hungry AI to detect climate tipping points is a paradox appeared first on Climate Home News.
Using energy-hungry AI to detect climate tipping points is a paradox
Computer models that use artificial intelligence (AI) cannot forecast record-breaking weather as well as traditional climate models, according to a new study.
It is well established that AI climate models have surpassed traditional, physics-based climate models for some aspects of weather forecasting.
However, new research published in Science Advances finds that AI models still “underperform” in forecasting record-breaking extreme weather events.
The authors tested how well both AI and traditional weather models could simulate thousands of record-breaking hot, cold and windy events that were recorded in 2018 and 2020.
They find that AI models underestimate both the frequency and intensity of record-breaking events.
A study author tells Carbon Brief that the analysis is a “warning shot” against replacing traditional models with AI models for weather forecasting “too quickly”.
AI weather forecasts
Extreme weather events, such as floods, heatwaves and storms, drive hundreds of billions of dollars in damages every year through the destruction of cropland, impacts on infrastructure and the loss of human life.
Many governments have developed early warning systems to prepare the general public and mobilise disaster response teams for imminent extreme weather events. These systems have been shown to minimise damages and save lives.
For decades, scientists have used numerical weather prediction models to simulate the weather days, or weeks, in advance.
These models rely on a series of complex equations that reproduce processes in the atmosphere and ocean. The equations are rooted in fundamental laws of physics, based on decades of research by climate scientists. As a result, these models are referred to as “physics-based” models.
However, AI-based climate models are gaining popularity as an alternative for weather forecasting.
Instead of using physics, these models use a statistical approach. Scientists present AI models with a large batch of historical weather data, known as training data, which teaches the model to recognise patterns and make predictions.
To produce a new forecast, the AI model draws on this bank of knowledge and follows the patterns that it knows.
There are many advantages to AI weather forecasts. For example, they use less computing power than physics-based models, because they do not have to run thousands of mathematical equations.
Furthermore, many AI models have been found to perform better than traditional physics-based models at weather forecasts.
However, these models also have drawbacks.
Study author Prof Sebastian Engelke, a professor at the research institute for statistics and information science at the University of Geneva, tells Carbon Brief that AI models “depend strongly on the training data” and are “relatively constrained to the range of this dataset”.
In other words, AI models struggle to simulate brand new weather patterns, instead tending forecast events of a similar strength to those seen before. As a result, it is unclear whether AI models can simulate unprecedented, record-breaking extreme events that, by definition, have never been seen before.
Record-breaking extremes
Extreme weather events are becoming more intense and frequent as the climate warms. Record-shattering extremes – those that break existing records by large margins – are also becoming more regular.
For example, during a 2021 heatwave in north-western US and Canada, local temperature records were broken by up to 5C. According to one study, the heatwave would have been “impossible” without human-caused climate change.
The new study explores how accurately AI and physics-based models can forecast such record-breaking extremes.
First, the authors identified every heat, cold and wind event in 2018 and 2020 that broke a record previously set between 1979 and 2017. (They chose these years due to data availability.) The authors use ERA5 reanalysis data to identify these records.
This produced a large sample size of record-breaking events. For the year 2020, the authors identified around 160,000 heat, 33,000 cold and 53,000 wind records, spread across different seasons and world regions.
For their traditional, physics-based model, the authors selected the High RESolution forecast model from the Integrated Forecasting System of the European Centre for Medium-Range Weather Forecasts. This is “widely considered as the leading physics-based numerical weather prediction model”, according to the paper.
They also selected three “leading” AI weather models – the GraphCast model from Google Deepmind, Pangu-Weather developed by Huawei Cloud and the Fuxi model, developed by a team from Shanghai.
The authors then assessed how accurately each model could forecast the extremes observed in the year 2020.
Dr Zhongwei Zhang is the lead author on the study and a researcher at Karlsruhe Institute of Technology. He tells Carbon Brief that many AI weather forecast models were built for “general weather conditions”, as they use all historical weather data to train the models. Meanwhile, forecasting extremes is considered a “secondary task” by the models.
The authors explored a range of different “lead times” – in other words, how far into the future the model is forecasting. For example, a lead time of two days could mean the model uses the weather conditions at midnight on 1 January to simulate weather conditions at midnight on 3 January.
The plot below shows how accurately the models forecasted all extreme events (left) and heat extremes (right) under different lead times. This is measured using “root mean square error” – a metric of how accurate a model is, where a lower value indicates lower error and higher accuracy.
The chart on the left shows how two of the AI models (blue and green) performed better than the physics-based model (black) when forecasting all weather across the year 2020.
However, the chart on the right illustrates how the physics-based model (black) performed better than all three AI models (blue, red and green) when it came to forecasting heat extremes.
The authors note that the performance gap between AI and physics-based models is widest for lower lead times, indicating that AI models have greater difficulty making predictions in the near future.
They find similar results for cold and wind records.
In addition, the authors find that AI models generally “underpredict” temperature during heat records and “overpredict” during cold records.
The study finds that the larger the margin that the record is broken by, the less well the AI model predicts the intensity of the event.
‘Warning shot’
Study author Prof Erich Fischer is a climate scientist at ETH Zurich and a Carbon Brief contributing editor. He tells Carbon Brief that the result is “not unexpected”.
He adds that the analysis is a “warning shot” against replacing traditional models with AI models for weather forecasting “too quickly”.
The analysis, he continues, is a “warning shot” against replacing traditional models with AI models for weather forecasting “too quickly”.
AI models are likely to continue to improve, but scientists should “not yet” fully replace traditional forecasting models with AI ones, according to Fischer.
He explains that accurate forecasts are “most needed” in the runup to potential record-breaking extremes, because they are the trigger for early warning systems that help minimise damages caused by extreme weather.
Leonardo Olivetti is a PhD student at Uppsala University, who has published work on AI weather forecasting and was not involved in the study.
He tells Carbon Brief that “many other studies” have identified issues with using AI models for “extremes”, but this paper is novel for its specific focus on extremes.
Olivetti notes that AI models are already used alongside physics-based models at “some of the major weather forecasting centres around the world”. However, the study results suggest “caution against relying too heavily on these [AI] models”, he says.
Prof Martin Schultz, a professor in computational earth system science at the University of Cologne who was not involved in the study, tells Carbon Brief that the results of the analysis are “very interesting, but not too surprising”.
He adds that the study “justifies the continued use of classical numerical weather models in operational forecasts, in spite of their tremendous computational costs”.
Advances in forecasting
The field of AI weather forecasting is evolving rapidly.
Olivetti notes that the three AI models tested in the study are an “older generation” of AI models. In the last two years, newer “probabilistic” forecast models have emerged that “claim to better capture extremes”, he explains.
The three AI models used in the analysis are “deterministic”, meaning that they only simulate one possible future outcome.
In contrast, study author Engelke tells Carbon Brief that probabilistic models “create several possible future states of the weather” and are therefore more likely to capture record-breaking extremes.
Engelke says it is “important” to evaluate the newer generation of models for their ability to forecast weather extremes.
He adds that this paper has set out a “protocol” for testing the ability of AI models to predict unprecedented extreme events, which he hopes other researchers will go on to use.
The study says that another “promising direction” for future research is to develop models that combine aspects of traditional, physics-based weather forecasts with AI models.
Engelke says this approach would be “best of both worlds”, as it would combine the ability of physics-based models to simulate record-breaking weather with the computational efficiency of AI models.
Dr Kyle Hilburn, a research scientist at Colorado State University, notes that the study does not address extreme rainfall, which he says “presents challenges for both modelling and observing”. This, he says, is an “important” area for future research.
The post Traditional models still ‘outperform AI’ for extreme weather forecasts appeared first on Carbon Brief.
Traditional models still ‘outperform AI’ for extreme weather forecasts
Christopher Wright is the principal analyst at CarbonBridge, a decarbonisation consulting firm.
The Santa Marta Conference has rightly been hailed as a pivotal opportunity to re-imagine the world’s relationship with fossil fuels. However, the sixty-odd countries gathered this week represent only 15% of the world’s total fossil fuel production, and a small but critical handful of nations in attendance remain deeply committed to expanding their fossil fuel output.
While the discussions at Santa Marta have focused on overcoming economic dependency on fossil fuels, the reality on the ground for many of these countries is that fossil fuel production continues to rise. Despite the rapid global growth of renewable electrification, fossil fuel output has similarly increased.
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This trend is evident even among the countries gathered at Santa Marta, where according to a CarbonBridge analysis, net fossil fuel production has grown over the last five years, particularly driven by expansions in oil and gas output.
Across all countries gathered in Santa Marta, approximately 14 countries are responsible for the lion’s share of oil production, which has increased by 4% since 2020. Similarly, just eight countries account for 96% of the conference’s natural gas production, which has collectively grown by 5% over the past decade.
While coal production has seen a slight decline since 2020, recent production increases in Turkey and Pakistan, with renewed growth in Australia, could similarly see increased production in the near future.
However, most surprisingly, only six countries present at Santa Marta account for over 80% of fossil fuel production among all nations in attendance: Canada, Australia, Brasil, Mexico, Norway and Nigeria.
For these nations, the transition journey ahead is complex. All six countries are aiming to significantly expand renewable energy capacities, and Norway stands as a global leader in electric vehicle adoption.
However, fossil fuel production is not merely a domestic concern for these countries; it plays a central role in their international exports, and remains a foundational pillar of their economic utures. In fact, a deeper look into trends and regulatory frameworks across this suite of countries indicates that their current trajectories are geared toward continued fossil fuel expansion.
Canada
In Canada, oil and gas production continues to climb, with 2025 marking a year of record highs. Oil production rose by 4% to reach 5.34 million barrels per day (MMb/d), while natural gas production surged by 3.4%, reaching 8.2 billion gigajoules. And only yesterday, Shell made a $13.5 bln bet on Canada’s oil and gas future.
Led by Prime Minister Mark Carney, Canada is set to implement an industrial carbon pricing scheme and could double Canada’s clean energy capacity over the next two years. However, he has also been vocal about his support for new oil and gas expansions, new pipeline developments, and has even set a goal to transform Canada’s largely non-existent liquefied natural gas (LNG) industry over the next 15 years, with aspirations to rival the production capacity of the US by 2040.
Brazil
Brazil’s state-owned oil company Petrobras has committed to a massive USD $109 billion expansion of their production to 2030. This hefty investment follows a record 11% production increase in 2025, with Petrobras pumping out 3.77 million barrels per day. Despite hosting the UN climate negotiations last year and generating 89% of the country’s electricity from low-carbon sources in 2025, Brazil’s drive for fossil fuel expansion highlights the gap between national climate transitions and critical export opportunities.
Australia
Australia, the world’s second-largest coal exporter, faces a similar dislocation between its domestic electricity transition and its export economy, as it prepares to assume a leadership role at COP31. Australia is home to the world’s highest solar power per capita and leads the world in home battery rollouts. However, it remains critically dependent on fossil fuel exports, even as questions arise over long-term demand. Currently, gas export volumes, which dipped in 2025, are projected to reach record levels by 2027; pending legal action against the Barossa, Scarborough, and Browse expansions. While thermal coal production is projected to decline slightly through 2030, increases in metallurgical coal are expected to offset these declines, in part due to recent pro-mining regulatory shifts in Queensland.
Mexico
Mexico is one of three major oil producers that make up over 60% of the conference’s annual oil production. However, its oil industry recorded the largest output declines of any major producer in Santa Marta over the last decade. The state-owned oil company Pemex, currently carries close to $100 billion in debt, and was granted $12bn in debt support from the government last year. When combined with import shifts from the US, and potential competition from Venezuela, there is a real chance that Mexico’s oil production could decline further going forward. However, the goal right now from Pemex and the Mexican government, is to increase current production by close to 10% by 2030.
Nigeria
Nigeria’s national oil company, NNPCL, has similarly seen declines over the last decade, but is now pursuing a $60 billion partnership to expand its oil and gas output and solidify its role as one of Africa’s largest fossil fuel producers. This comes even as the federal government was granted $800,000 to explore opportunities to transition away from oil expansion last year.
Norway
In contrast to these countries, Norway stands as one of the few major oil producers at the conference projected to decrease its fossil fuel output. With a forecasted 15% reduction in oil and gas production by 2030, Norway appears to be taking early steps toward a transition. However, the decline in production is more a reflection of the age of its existing oil fields than a proactive shift in government policy. Despite acknowledging the need to diversify its economy, the Norwegian government continues to explore new oil and gas fields, plans to launch new licensing rounds, and hopes to spur on further oil and gas investments, which have almost doubled since 2017.
For these nations, the road ahead is fraught with complexities. While the Santa Marta conference offers an opportunity for dialogue, and renewable energies will undoubtedly continue to expand, the largest fossil fuel producers gathered in Colombia remain structurally focused on growth, rather than phase-downs.
Dollars and cents continue to drive economic decisions, especially in the midst of a global energy crisis. Despite growing calls to utilise this opportunity to reshape development pathways, countries most economically embedded in existing energy markets will need far more convincing, before turning their backs on billions in fossil fuel revenues.
The post Six nations at Santa Marta could shape fossil fuel futures appeared first on Climate Home News.
A group of leading climate scientists has called on governments to develop roadmaps for phasing out fossil fuels “anchored in science and justice”, alongside the launch of a separate panel of experts that will give scientific advice on how to navigate the energy transition.
Unveiled on Friday in Santa Marta, Colombia, a set of a dozen policy recommendations, summarising the Santa Marta Academic Dialogue, is intended to feed into ministerial discussions on equitable ways to reduce dependence on coal, oil and gas during next week’s “First Conference on Transitioning Away from Fossil Fuels”.
The policy insights urge countries to create “whole-of-government” plans to “dismantle legal, financial and political barriers” to the energy transition.
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Johan Rockström, director of the Potsdam Institute for Climate Impact Research (PIK), said the push for a global transition away from fossil fuels offers “a light in the tunnel” during a “very dark moment” of geopolitical conflict and climate extremes.
“Science is here to serve,” Rockström told a packed Santa Marta Theatre. “We’re today launching the Science Panel for the Global Energy Transition (SPGET) as a service, as a global common good for all countries, all sectors, all regions to connect to the best science enabling a transition away from fossil fuels.”
Draft roadmap for Colombia
Colombian Environment Minister Irene Vélez Torres said the new SPGET panel “addresses a longstanding shortcoming” in international climate science, by creating a scientific body dedicated solely to overcoming the world’s reliance on fossil fuels.
“It’s a first-of-its-kind, designed to organise in the next five years the scientific evidence that allows cities, regions, countries and coalitions to take the big leap,” Vélez told the event in Santa Marta.
As an example of how countries can move forward – even when their economies are closely tied to the production and use of dirty energy – a group of European scientists presented a draft roadmap to phase out fossil fuels in Colombia, with inputs from the Colombian government. It will be used as a basis for further consultation in the Latin American nation to define the way forward.
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Piers Forster, director of the Priestley Centre for Climate Futures at the University of Leeds and co‑author of the roadmap, said it shows “a clear pathway to economic and societal benefit”, with average annual investment of $10.6 billion producing net economic benefits of $23 billion per year by 2050.
The document says fossil fuels in Colombia can be phased out through energy efficiency measures, coupling renewable generation with energy storage, and switching to electrified transport. But, it adds, the government will need to plan for reduced revenue from fossil fuel exports, which roughly half by the mid-2030s.
“What matters now is moving beyond headline targets to create credible, policy-relevant roadmaps, enabling a just and effective transition,” Forster said in a statement. Brazil is also working on a national roadmap for its own economy, as well as leading a voluntary process to produce a global roadmap.
IPCC hobbled by politics
Currently, the world’s top climate science body – the Intergovernmental Panel on Climate Change (IPCC) – requires countries to sign off on each “summary for policymakers” of its flagship science reports. This has led to a politically fraught process that has increasingly seen some oil-producing governments making efforts to weaken its recommendations.
In a bid to focus scientific debates on the phase-out of fossil fuels, the new SPGET was created based on a mandate from last year’s COP30. It is also meant to come up with scientific recommendations at a faster pace than the IPCC’s seven-year cycle.
Natalie Jones, senior policy advisor at the International Institute of Sustainable Development (IISD), called the new scientific panel “historic”, as it will be “more specific, more targeted and potentially more agile” with its advice on phasing out coal, oil and gas than the IPCC’s exhaustive scientific synthesis reports.
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The panel will be co-chaired by Cameroonian economist Vera Songwe, PIK’s chief economist Ottmar Edenhofer and Gilberto M. Jannuzzi, professor of energy systems at Brazil’s Universidade Estadual de Campinas. It will be composed of between 50 and 100 scientists divided into four working groups: transition pathways, technological solutions, policies and finance.
Under the 12 insights for the Santa Marta process, the other group of scientists recommended banning new fossil fuel infrastructure, mandating “deep cuts” in methane emissions, implementing carbon levies on imports, and de-risking clean energy investments via interventions from central banks, among others.
Co-author Peter Newell, professor of international relations at the UK’s University of Sussex, said “there are many different challenges along the way – and not all of them have to do with lack of evidence”, but the phasing out of fossil fuels “is one part of the story and it’s important to address it”.
The original version of this story incorrectly reported that the new Science Panel for the Global Energy Transition had called on governments to develop roadmaps for phasing out fossil fuels “anchored in science and justice”. This appeal came from a separate group of scientists that worked on recommendations ahead of the Santa Marta conference. The article has now been amended.
The post Climate scientists call for fossil fuel transition roadmaps appeared first on Climate Home News.
https://www.climatechangenews.com/2026/04/25/climate-scientists-call-for-fossil-fuel-transition-roadmaps/
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